A sequential sampling-based Bayesian numerical method for reliability-based design optimization

verfasst von: Fangqi Hong, Pengfei Wei, Jiangfeng Fu, Michael Beer
Abstract: For efficiently solving the Reliability-Based Design Optimization (RBDO) problem with multi-modal, highly nonlinear and expensive-to-evaluate limit state functions (LSFs), a sequential sampling-based Bayesian active learning method is developed in this work. The penalty function method is embedded to transform the constrained optimization problem into a non-constrained one to reduce the model complexity. The proposed method for solving RBDO problems starts by training a Gaussian process (GP) model, in the augmented space of random and design variables. It is then based on an efficient sampling scheme for simulating the GP model, the adaptive Bayesian optimization (BO) and Bayesian reliability analysis (BRA) procedures are combined in a collaborative way for sequentially producing the joint training points. BO driven by expected improvement (EI) function is used for inferring the global optimum in the design space with global convergence, and the BRA equipped with U function is implemented for inferring the failure probabilities at the identified design points with the desired accuracy. The superiority of the proposed method is demonstrated with two numerical and two real-world engineering examples.
Organisationseinheit(en): Institut für Risiko und Zuverlässigkeit
Externe Organisation(en): Northwestern Polytechnical University
The University of Liverpool
Tongji University
Typ: Artikel
Journal: Reliability Engineering and System Safety
Band: 244
Anzahl der Seiten: 12
ISSN: 0951-8320
Publikationsdatum: 04.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Sicherheit, Risiko, Zuverlässigkeit und Qualität, Wirtschaftsingenieurwesen und Fertigungstechnik
Elektronische Version(en): https://doi.org/10.1016/j.ress.2024.109939 (Zugang: Geschlossen)

BibTeX

@article{2280a917e7ed4b2cb6f4c4687c04f157,
title = "A sequential sampling-based Bayesian numerical method for reliability-based design optimization",
abstract = "For efficiently solving the Reliability-Based Design Optimization (RBDO) problem with multi-modal, highly nonlinear and expensive-to-evaluate limit state functions (LSFs), a sequential sampling-based Bayesian active learning method is developed in this work. The penalty function method is embedded to transform the constrained optimization problem into a non-constrained one to reduce the model complexity. The proposed method for solving RBDO problems starts by training a Gaussian process (GP) model, in the augmented space of random and design variables. It is then based on an efficient sampling scheme for simulating the GP model, the adaptive Bayesian optimization (BO) and Bayesian reliability analysis (BRA) procedures are combined in a collaborative way for sequentially producing the joint training points. BO driven by expected improvement (EI) function is used for inferring the global optimum in the design space with global convergence, and the BRA equipped with U function is implemented for inferring the failure probabilities at the identified design points with the desired accuracy. The superiority of the proposed method is demonstrated with two numerical and two real-world engineering examples.",
keywords = "Acquisition function, Bayesian optimization, Bayesian reliability analysis, Gaussian process simulation, Reliability-based design optimization",
author = "Fangqi Hong and Pengfei Wei and Jiangfeng Fu and Michael Beer",
note = "Funding Information: This work is supported by the National Natural Science Foundation of China under grant number 72171194 , and the Sino-German Mobility Programme under grant number M-0175 (2021–2024) . ",
year = "2024",
month = apr,
doi = "10.1016/j.ress.2024.109939",
language = "English",
volume = "244",
journal = "Reliability Engineering and System Safety",
issn = "0951-8320",
publisher = "Elsevier Ltd.",
}